// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT

#include <hip/hip_runtime.h>

#include <cstring>
#include <iostream>
#include <ostream>
#include <string>
#include <tuple>
#include <type_traits>

#include "ck_tile/host.hpp"
#include "mx_flatmm.hpp"

template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
    return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
                                                 ck_tile::tensor_layout::gemm::RowMajor>>{};
}

template <typename FlatmmConfig,
          typename ADataType,
          typename BDataType,
          typename DsDatatype,
          typename AccDataType,
          typename CDataType,
          typename ALayout,
          typename BLayout,
          typename DsLayout,
          typename CLayout,
          typename ScaleA,
          typename ScaleB,
          bool UsePersistentKernel = false,
          typename CDEElementWise  = ck_tile::element_wise::PassThrough>
float invoke_mx_flatmm(ck_tile::DeviceMem& a_dev_buf,
                       ck_tile::DeviceMem& b_shuffle_dev_buf,
                       ck_tile::DeviceMem& c_dev_buf,
                       ck_tile::index_t M,
                       ck_tile::index_t N,
                       ck_tile::index_t K,
                       ck_tile::index_t stride_A,
                       ck_tile::index_t stride_B,
                       ck_tile::index_t stride_C,
                       ck_tile::index_t kbatch,
                       ScaleA scale_a,
                       ScaleB scale_b,
                       int n_warmup,
                       int n_repeat)
{
    ck_tile::ScaleFlatmmHostArgs<ScaleA, ScaleB> args = {a_dev_buf.GetDeviceBuffer(),
                                                         b_shuffle_dev_buf.GetDeviceBuffer(),
                                                         {},
                                                         c_dev_buf.GetDeviceBuffer(),
                                                         kbatch,
                                                         M,
                                                         N,
                                                         K,
                                                         stride_A,
                                                         stride_B,
                                                         {},
                                                         stride_C,
                                                         scale_a,
                                                         scale_b};

    using FlatmmShape = ck_tile::TileGemmShape<
        ck_tile::sequence<FlatmmConfig::M_Tile, FlatmmConfig::N_Tile, FlatmmConfig::K_Tile>,
        ck_tile::sequence<FlatmmConfig::M_Warp, FlatmmConfig::N_Warp, FlatmmConfig::K_Warp>,
        ck_tile::sequence<FlatmmConfig::M_Warp_Tile,
                          FlatmmConfig::N_Warp_Tile,
                          FlatmmConfig::K_Warp_Tile>>;

    using TilePartitioner =
        ck_tile::GemmSpatiallyLocalTilePartitioner<FlatmmShape,
                                                   FlatmmConfig::TileParitionerGroupNum,
                                                   FlatmmConfig::TileParitionerM01>;

    using Traits = ck_tile::TileGemmTraits<FlatmmConfig::kPadM,
                                           FlatmmConfig::kPadN,
                                           FlatmmConfig::kPadK,
                                           ALayout,
                                           BLayout,
                                           CLayout,
                                           FlatmmConfig::NumWaveGroups>;
    using GemmPipelineProblem =
        ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, FlatmmShape, Traits>;

    using BaseFlatmmPipeline = ck_tile::BaseFlatmmPipelineAGmemBGmemCRegV1<GemmPipelineProblem>;

    const ck_tile::index_t k_grain     = args.k_batch * FlatmmConfig::K_Tile;
    const ck_tile::index_t k_split     = (K + k_grain - 1) / k_grain * FlatmmConfig::K_Tile;
    const ck_tile::index_t num_loop    = TilePartitioner::GetLoopNum(k_split);
    const bool has_hot_loop            = BaseFlatmmPipeline::BlockHasHotloop(num_loop);
    const ck_tile::TailNumber tail_num = BaseFlatmmPipeline::GetBlockLoopTailNum(num_loop);

    float ave_time = BaseFlatmmPipeline::template TailHandler<true>(
        [&](auto has_hot_loop_, auto tail_num_) {
            constexpr auto has_hot_loop_v = has_hot_loop_.value;
            constexpr auto tail_num_v     = tail_num_.value;
            auto invoke_splitk_path       = [&](auto split_k_) {
                return mx_flatmm_calc<FlatmmConfig,
                                            ADataType,
                                            BDataType,
                                            DsDatatype,
                                            AccDataType,
                                            CDataType,
                                            ALayout,
                                            BLayout,
                                            DsLayout,
                                            CLayout,
                                            ScaleA,
                                            ScaleB,
                                            UsePersistentKernel,
                                            CDEElementWise,
                                            split_k_.value,
                                            has_hot_loop_v,
                                            tail_num_v>(
                    args,
                    ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
            };
            return (args.k_batch == 1) ? invoke_splitk_path(std::false_type{})
                                       : invoke_splitk_path(std::true_type{});
        },
        has_hot_loop,
        tail_num);

    constexpr int APackedSize = ck_tile::numeric_traits<ADataType>::PackedSize;
    constexpr int BPackedSize = ck_tile::numeric_traits<BDataType>::PackedSize;

    std::size_t flop     = std::size_t(2) * M * N * K + std::size_t(2) * M * N * K / 32;
    std::size_t num_byte = sizeof(ADataType) * M * K / APackedSize +
                           sizeof(BDataType) * N * K / BPackedSize + sizeof(CDataType) * M * N +
                           sizeof(ck_tile::e8m0_t) * M * K / 32 +
                           sizeof(ck_tile::e8m0_t) * N * K / 32;
    float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
    float gb_per_sec = num_byte / 1.E6 / ave_time;

    std::cout << "Run " << ck_tile::gemm_prec_str<ADataType, BDataType>() << " Flatmm kernel " //
              << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << stride_A
              << " StrideB = " << stride_B << " StrideC = " << stride_C << " : " << ave_time
              << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;

    return ave_time;
}

auto create_args(int argc, char* argv[])
{
    ck_tile::ArgParser arg_parser;
    arg_parser.insert("m", "32", "m dimension")
        .insert("n", "128", "n dimension")
        .insert("k", "256", "k dimension")
        .insert("a_layout", "R", "A tensor data layout - Row by default")
        .insert("b_layout", "C", "B tensor data layout - Row by default")
        .insert("c_layout", "R", "C tensor data layout - Row by default")
        .insert("stride_a", "0", "Tensor A stride")
        .insert("stride_b", "0", "Tensor B stride")
        .insert("stride_c", "0", "Tensor C stride")
        .insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
        .insert(
            "mx_prec", "fp4xfp4", "data type for activation and weight, support: fp4xfp4, fp8xfp8")
        .insert("warmup", "50", "number of iterations before benchmark the kernel")
        .insert("repeat", "100", "number of iterations to benchmark the kernel")
        .insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
        .insert("split_k", "1", "splitK value")
        .insert("init", "0", "0:random, 1:constant(1)")
        .insert("persistent", "0", "0: no persistent, 1: persistent kernel")
        .insert("warp_tile",
                "0",
                "0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)");
    bool result = arg_parser.parse(argc, argv);
    return std::make_tuple(result, arg_parser);
}

template <ck_tile::index_t N_Warp_Tile, typename dtype>
auto preShuffleWeight(ck_tile::HostTensor<dtype>& src)
{
    auto src_lengths          = src.get_lengths();
    const int K               = src_lengths[0];
    const int N               = src_lengths[1];
    constexpr int packed_size = ck_tile::numeric_traits<dtype>::PackedSize;
    int KPack                 = 16 * packed_size; // fp4:32 or fp8:16
    int NLane                 = N_Warp_Tile;
    int KLane                 = 64 / NLane;
    int K0                    = K / (KLane * KPack);

    ck_tile::HostTensor<dtype> shuffled(ck_tile::HostTensorDescriptor({N * K}, {1}));

    // K -> K0 KLane KPack
    // N -> N0 NLane
    // N, K -> N0 K0 KLane NLane KPack
    for(int n = 0; n < N; ++n)
    {
        for(int k = 0; k < K; k += packed_size)
        {
            int n0 = n / NLane;
            int n1 = n % NLane;

            int k0    = k / (KLane * KPack);
            int tempk = k % (KLane * KPack);
            int k1    = tempk / KPack;
            int k2    = tempk % KPack;

            int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
                              k1 * KPack * NLane + n1 * KPack + k2;

            shuffled(outputIndex) = src(k, n);
        }
    }
    return shuffled;
}

template <class FlatmmConfig, bool KLast, typename dtype>
auto preShuffleScale(ck_tile::HostTensor<dtype>& src)
{
    auto src_lengths = src.get_lengths();
    const auto MN    = KLast ? src_lengths[0] : src_lengths[1];
    const auto K     = KLast ? src_lengths[1] : src_lengths[0];

    size_t MNXdlPack   = 2;
    size_t KXdlPack    = 2;
    size_t XdlMNThread = FlatmmConfig::N_Warp_Tile; // 16
    size_t XdlKThread  = 64 / XdlMNThread;

    const auto MN_Paded = ck_tile::integer_least_multiple(MN, XdlMNThread * MNXdlPack);

    ck_tile::HostTensor<dtype> shuffled(ck_tile::HostTensorDescriptor({MN_Paded * K}, {1}));

    size_t K0 = K / KXdlPack / XdlKThread; // KRepeat

    // The 4 16x128 building blocks will be packed into 1 32x256 for F4
    // The 8 16x16x128 mfma will be packed into 1 32x32x256 for F4

    // unfold the MN32xK(256/32) scale buffer
    //    4            16             2           2
    // To XdlKThread-> XdlMNThread -> KXdlPack -> MNXdlPack
    // Then, MNRepeat->KRepeat

    for(size_t n = 0; n < MN_Paded; ++n)
    {
        for(size_t k = 0; k < K; ++k)
        {
            auto n0    = n / (XdlMNThread * MNXdlPack); // i MNRepeat
            auto tempn = n % (XdlMNThread * MNXdlPack);
            auto n1    = tempn % XdlMNThread; // i XdlMNThread
            auto n2    = tempn / XdlMNThread; // i MNXdlPack

            auto k0    = k / (XdlKThread * KXdlPack); // i KRepeat
            auto tempk = k % (XdlKThread * KXdlPack);
            auto k1    = tempk % XdlKThread; // i XdlKThread
            auto k2    = tempk / XdlKThread; // i KXdlPack

            auto outputIndex = n0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread * K0 +
                               k0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread +
                               k1 * MNXdlPack * KXdlPack * XdlMNThread + n1 * MNXdlPack * KXdlPack +
                               k2 * MNXdlPack + n2;

            if constexpr(KLast)
                shuffled(outputIndex) = n < MN ? src(n, k) : dtype{};
            else
                shuffled(outputIndex) = n < MN ? src(k, n) : dtype{};
        }
    }
    return shuffled;
}

#include "run_mx_flatmm.inc"

int run_mx_flatmm_example(int argc, char* argv[])
{
    auto [result, arg_parser] = create_args(argc, argv);
    if(!result)
        return -1;

    using Row = ck_tile::tensor_layout::gemm::RowMajor;
    using Col = ck_tile::tensor_layout::gemm::ColumnMajor;

    std::string mx_prec  = arg_parser.get_str("mx_prec");
    std::string a_layout = arg_parser.get_str("a_layout");
    std::string b_layout = arg_parser.get_str("b_layout");
    int persistent_opt   = arg_parser.get_int("persistent");

    if(a_layout == "R" && b_layout == "C")
    {
        if(mx_prec == "fp4" || mx_prec == "fp4xfp4")
        {
            if(persistent_opt == 0)
                return run_mx_flatmm_with_layouts<ck_tile::pk_fp4_t,
                                                  ck_tile::pk_fp4_t,
                                                  ck_tile::fp16_t,
                                                  MXfp4_FlatmmConfig16,
                                                  false>(argc, argv, Row{}, Col{}, Row{});
            else
                throw std::runtime_error("Only non-persistent kernels are supported currently!");
        }
        else if(mx_prec == "fp6" || mx_prec == "fp6xfp6")
        {
            throw std::runtime_error("fp6xfp6 is not supported.");
        }
        else if(mx_prec == "fp8" || mx_prec == "fp8xfp8")
        {
            if(persistent_opt == 0)
                return run_mx_flatmm_with_layouts<ck_tile::fp8_t,
                                                  ck_tile::fp8_t,
                                                  ck_tile::fp16_t,
                                                  MXfp8_FlatmmConfig16,
                                                  false>(argc, argv, Row{}, Col{}, Row{});
            else
                throw std::runtime_error("Only support non-persistent kernel now!");
        }
        else
        {
            throw std::runtime_error("Unsupported data_type!");
        }
    }
    else
    {
        throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
    }
}

int main(int argc, char* argv[])
{
    auto [result, arg_parser] = create_args(argc, argv);
    if(!result)
        return EXIT_FAILURE;
    try
    {
        int warp_tile = arg_parser.get_int("warp_tile");
        if(warp_tile == 0)
        {
            return run_mx_flatmm_example(argc, argv);
        }
        else if(warp_tile == 1)
        {
            throw std::runtime_error("Only support MFMA_16x16x128 now!");
        }
        else
        {
            throw std::runtime_error("Unsupported warp_tile!");
        }
    }
    catch(const std::runtime_error& e)
    {
        std::cerr << "Runtime error: " << e.what() << '\n';
        return EXIT_FAILURE;
    }
}
